The Tonkolili iron field in northern Sierra Leone has the largest known iron ore deposit in Africa. It occurs in a greenstone belt in an Achaean granitic basement. This study focused mainly on mapping areas with iron-oxide and hydroxyl bearing minerals, and identifying potential areas for haematite mineralization and banded iron formations (BIFs) in Tonkolili. The predominant mineral assemblage at the surface (laterite duricrust) of this iron field is haematitegoethite- limonite ±magnetite. The mineralization occurs in quartzitic banded ironstones, layered amphibolites, granites, schists and hornblendites. In this study, Crosta techniques were applied on Enhanced Thematic Mapper (ETM+) data to enhance areas with alteration minerals and target potential areas of haematite and BIF units in the Tonkolili iron field. Synthetic analysis shows that alteration zones mapped herein are consistent with the already discovered magnetite BIFs in Tonkolili. Based on the overlaps of the simplified geological map and the remote sensing-based alteration mineral maps obtained in this study, three new haematite prospects were inferred within, and one new haematite prospect was inferred outside the tenement boundary of the Tonkolili exploration license. As the primary iron mineral in Tonkolili is magnetite, the study concludes that, these haematite prospects could also be underlain by magnetite BIFs. This study also concludes that, the application of Crosta techniques on ETM+ data is effective not only in mapping iron-oxide and hydroxyl alterations but can also provide a basis for inferring areas of potential iron resources in Algoma-type banded iron formations (BIFs), such as those in the Tonkolili field.

Shadow is one of the basic characteristics in urban remote sensed imagery. It affects the extraction of object’s edge, identification of objects and registration of images, so shadow detection has a great importance in urban remote sensing. In this paper, a kind of method with HSV is proposed to detect shadow from the color high resolution remote sensing imagery mainly through a series of processing steps including twice HSV transformation, self-adaptive segmentation, morphological closing operation and little area removing. At last, the ratio of the shadow is achieved according to the shadow area statistical analysis. The experiments show that the approach can detect the shadow accurately and availably.

This article explores Automatic generalization of Map Resident in digital environment that include creating neighboring network based on Delaunay triangulation, constructing minimum spanning tree from neighboring network, to determine the polygon grouping strategy based on minimum spanning tree, then explore polygon merging and removing algorithm. This method can retain important features of geographic features while maintaining a good capacity and readability, finally obtaining multi-scale maps.

Marine oil pollution is one of the most serious pollutants on the damage to the contemporary marine environment, with the characteristics of a wide range of proliferation, which is difficult to control and eliminate. As a result, marine oil pollution has caused huge economic losses. The remote sensing sensors can detect and record the spectral information of sea film and background seawater. Here we chose to use 250-resolution MODIS data in the area of Dalian Xingang, China where ill spill case was happened on April.4th, 2005. Based on the image pre-processing and enhanced image processing, the spectral features of different bands were analyzed. More obvious characteristics of the spectral range of film was obtained. The oil-water contrast was calculated to evaluate the feature of oil at different spectral band. The result indicates that IR band has the maximum value of reflective. So band ratio was used between 400nm and 800nm and the original radiance images were used between 800nm and 2130nm. In order to get the most obvious images of entropy windows of different sizes were tested in order to decide the optimum window. At last, a FCM fuzzy clustering method and image texture analysis was combined for the MODIS images of the oil spill area segmentation. At last, the oil spill zone was estimated, the results were satisfied.

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and design of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it's difficult to discriminate forest tree species using multi-spectral image. The airborne hyperspectral images can achieve the high spatial and spectral resolution imagery of forest canopy, so it will good for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. Firstly, we use the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. And secondly, we use the grey level co-occurrence matrix (GLCM) to extract the texture features of forest tree canopy from the hyper-spectral image, and thirdly we fused the texture and the spectral features of forest canopy to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92%. It was also confirm that combine the spatial and spectral information can improve the accuracy of tree species classification.

Lake water temperature is one of the most important parameters determining ecological conditions in lake water. With the recent development of satellite remote sensing, remotely sensed data instead of traditional sampling measurement can be used to retrieve the lake surface temperature. The East Lake located in the Wuhan city was selected as research region in this paper. The mono window algorithm has been applied to retrieve the lake water temperature of East lake basin with Landsat TM data. Through three groups of field survey data, the outcome shows that the retrieval results using the mono window model are quite approximate to the same period of the experimental region historical temperature data. So, it is feasible to utilize the remote sensing method to obtain the lake temperature. Meanwhile, the retrieval results also demonstrate that the East Lake surface temperatures from different years have the similar distribution regularity. Generally speaking, the temperature of the lake center is higher than the surrounding area. The west of lake is mostly higher than the east mainly due to the vegetation density and urbanization distribution condition. This conclusion is important to the further study on monitoring the East Lake temperature particularly in large scale.

Optical flow is a critical component of video processing applications, e.g. for tasks such as object tracking, segmentation, and navigation. In this paper, we present a novel concept entropy flow based on the visual mechanism of bee. It not only generates motion fields form real sequences but also speed up the calculation of optical flow. The introduction of entropy image has shown the information distribution for the given image, and the optical flow algorithm is utilized to get the entropy flow. Global motion estimation and an auto-selecting algorithm of assessment threshold are employed to give a motion image and then the accuracy is improved.

Digital camera is non-metric camera. Its geometric calibration, including the determination of interior orientation elements and distortion parameters, is the base of high precision photogrammetry. In this paper, a laboratory geometric calibration system of digital cameras is developed. This system uses a collimator and a star tester as the target generator. After high precision positioning of targets and corresponding angles of parallel lights, geometric calibration can be accomplished according to the method of this paper. Experiments are taken out based on this system using two kinds of mainstream digital aerial cameras, Cannon EOS 5D Mark II and Hasselblad H3D, and the repeatability experiment is also taken out. Experiment results show that this system can satisfy the calibration requirements of photogrammetric application and the correctness and reliability of the measure precision of this system is verified.

In order to enhance the accuracy of hyperspectral remote sensing classification, a classification method based on SVM with end-member extraction is presented. Firstly, the end-members are extracted using pure pixel index approach, and then the ground target is identified based on the spectral feature fitting , followed by the spectral classification of the hyperspectral remote sensing images with the Support Vector Machines. The experiment results indicated that the validity and efficiency of our method are more accurately than the traditional SVM solutions which simply use the regions of interest selected from image as the training samples.

Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. We have developed a compact novel instrument that can not only provide imaging capability, bust also one that provides spectral capability of the field of view (FOV) center under the imaging guided. The absolute radiance accuracy of an instrument is one of its fundamental characteristics. In order to meet the highest radiometric precision and accuracy, we give two specific calibration methods: two-point calibration and multi-point segmentation calibration. This paper presents the results of the analysis of the radiometric calibration of this instrument, with emphasis on the temporal behavior of the instrument response.

Aimed to the limitation of present anomaly detection algorism under clutter background for multi-spectral remote sensing data, especially for the situations of dense spread target and exist different attributive of background objects, a bio-inspired anomaly detection algorithm was proposed. Simulate the information processing and fusion mechanism of fly multi-apertures vision system, multi-level background model was proposed to analysis and describe feature of clutter background. Then the threshold value can be chose adaptively according to the level of background model. The proposed algorithm didn’t need the prior knowledge about anomaly, and avoids the choosing of the background widow size. A fusion mechanism was proposed to fuse the different detection results with different level background model. Simulation experiment validated the effectiveness of proposed method.

Polar metric synthetic aperture radar (PolSAR) image classification is an important technique in the remote sensing area, has been deeply studied for a couple of decades. This paper proposes a new approach for segmentation and classification of PolSAR datain two steps. First, segmentation is performed based on spectral graph partitioning using edge information. Graph partitioning process is completed using the normalized cut criterion. Then, classification is performed based on the object level. We use Cloude and Pottier‟s method to initially classify the PolSAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The advantages of this method are the automated classification, and the interpretation of each class based on the region‟s scattering mechanism. We tested this object-based analysis on our study area. It showed that this result well overcome the pepper-sault phenomenon appearing in the one using traditional pixel-based method, providing robust performance and the results more understandable and easier for further analyses

This paper present an effective method for ship detection using optical flow and saliency methods from optical satellite images, which can be able to identify more than one ship targets in the complex dynamic sea background and succeeds to reduce the false positive rate compared to traditional methods. In this paper, moving targets in the image are highlighted through the classical optical flow method, and the dynamic waves are restrained by combining the state-of-art saliency method. We make the best of the low-level (size, color, etc.) and high-level (adjacent frames information, etc.) features of image, which can adapt to different dynamic background situation. Compared to existing method, experimental results demonstrate the robustness of the proposed method with high performance.

Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the effect of the climate change. An unbelievable situation in mapping snow cover is the existence of clouds. Clouds can easily be found in any image from satellite, because clouds are bright and white in the visible wavelengths. But it is not the case when there is snow or ice in the background. It is similar spectral appearance of snow and clouds. Many cloud decision methods are built on decision trees. The decision trees were designed based on empirical studies and simulations. In this paper a classification trees were used to build the decision tree. And then with a great deal repeating scenes coming from the same area the cloud pixel can be replaced by “its” real surface types, such as snow pixel or vegetation or water. The effect of the cloud can be distinguished in the short wave infrared. The results show that most cloud coverage being removed. A validation was carried out for all subsequent steps. It led to the removal of all remaining cloud cover. The results show that the decision tree method performed satisfied.

Along with the rapid development of urbanization since 1980s, immense changes of the land use/cover have been caused a series of problems of ecological environment, such as farmland decreasing, natural vegetation damage, construction land expansion, land desertification and salinization, and so on. The research on the changes and driving forces of spatial pattern of land use/cover by remote sensing is conducive to master the influences on ecosystem from natural factors and human factors and accelerate sustainable development of ecological environment. The LandSat MSS/TM/ETM+ images were used in the paper. Taken support vector machine (SVM) as classifier, the supervised classification was carried out to extract the spatial distribution of each land cover types in 1978, 1992, 2000 and 2010. By calculating the transition matrix among four result images, the changes of spatial patterns of land cover in Beijing in recent thirty years was analyzed from numeral and spatial dynamics. Result showed that the land use/cover in Beijing region had changed a great deal from 1978 to 2010. The farmland area and unused land area were decreasing with a range more than 40% in recent 32 years, while the urban area and forest area were increasing with a range more than 35%. Most of the farmland was transformed into urban land and forest, while the grassland was transformed into farmland. The input urban area was mainly originated from farmland, while the output was grassland. It indicated that the urbanization and afforestation were the two primary drivers of land use/cover change in Beijing region in recent thirty years.

Satellite altimeters are widely used in the research of ocean dynamics. Data fusion methods such as Kriging method, successive correction method and inverse distance to a power method are used in this paper, and the appropriate parameters for these methods are achieved for the fusion of sea surface wind speed (SSW) data from GFO, Jason-1, Jason-2 and Envisat altimeters in China Seas and nearby. It is shown that (1) SSW fusion needs at least 3 satellites; (2) different methods have little impact on the merged result when using enough data; (3) the characteristics and distribution of result are agreed well with the statistical data in the study area.

The floods due to dam failure usually do great harm to human beings and social economy. The aim of this paper is to apply geographical information system (GIS) technology, and the loss estimation model to estimate the loss of the hypothetical dam break in Lushui reservoir basin. The general predictive loss evaluation scheme of dam-break flood is illustrated. The empirical formula method is used to determine the flood submerged region. The final loss assessment result is mapped out on GIS software. The result shows that the combination of GIS and loss estimation model can make the overall procedure for loss assessment efficient and effective, which could do favor to long-term basin flood control planning and disaster preparedness.

Recent years, China’s rapid economic development speeds up the process of urbanization, the most prominent is the rapid expansion of urban land. In this paper, remote sensing, GIS and statistical analysis techniques are used to analyze the dynamic process of land development of Wuhan city from 1995 to 2010, and its causes. Then the effectiveness of the urban master plan of Wuhan city in 1996 is evaluated. Finally, we analyze the possible reasons for the failure of urban planning, which will provide a reference for the future urban planning and management of Wuhan city.

South China Sea (SCS) is one of the most active areas for internal waves (IW) occurrences in the world. These waves generated at the Luzon Strait, propagate westward into the SCS, and dissipate on the continental shelf after persisting for more than 4 days. IW phase speed is an important parameter to study the evolution of IWs and the ocean interior characteristics. In this paper, we analyzed one pair of SAR/MODIS images containing same IW signatures in the SCS spanning from 19N to 23N and from 115E to 118E. The SAR and MODIS images were taken at 02:07:52 and 02:50:00 UTC on 22 April 2007, respectively. First, the SAR and MODIS images are calibrated and geo-referenced. By overlaying images in a GIS system, we are able to track the same wave-crest displacement between the time intervals of the image pair, and thus, derive the phase speeds of IWs at different locations. We show that the phase speeds of individual wave packets can be estimated accurately using this pair of MODIS/ASAR images separated in time by 42 minutes and 8 seconds. Furthermore, we find that there is an inseparable relationship between IW phase speed and water depth. The IW phase speeds are in proportion to the water depths along their pathways. In conclusion, we present a new multiple-sensor image fusion technique in a GIS environment to extract IW geolocation information and derive their propagation phase speeds.

Markov model is found to be beneficial in describing and analyzing land cover change process. The probability of transition between each pair of states is recorded as an element of a transition probability matrix, which is the key factor to obtain a higher precision of prediction in Markov model. In this study, a combined use of RS, GIS, Markov stochastic modeling and Monte Carlo simulating techniques are employed in analyzing and prediction land use/cover changes in Wuhan city. The results indicate that the transition probability matrix derived from Monte Carlo experiment is more accurate for land use prediction, and the prediction results of land use change show that there urban growth is has notable, area of forest land continued decreasing, and that the land use/cover change process would be stable in the future. The study demonstrates remote sensing image is an effective data source and statistical information of land use is a valid supplement for land use/land cover research. Integration of these two kinds of data in Markov - Monte Carlo method can adjust the basis of the same observation time when images are not available every year or at a constant time interval in LUCC modeling. Land use/land cover change information from the prediction results will be beneficial in describing, analyzing the change process of land structure in Wuhan city in next 20 years.

Since 1972, Landsat program has experienced six successful missions that have contributed to nearly 40 years record of Earth Observations for monitoring the land cover and change dynamics. The successful launch of the Landsat Data Continuity Mission (LDCM, now named Landsat 8) on February 11, 2013 continues the mission of collecting images of the Earth with an open (free) data policy. Landsat 8 carries two push broom sensors: the Operational Land Imager (OLI) will collect data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS) will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. However, cloud and associated cloud shadows frequently obscure the detection of land surface and restrict the the analysis of change trends over time. This paper presents a new method to detect and remove cloud and cloud shadows using the Landsat 8 first Image data (WRS2: Path/Row =33/32, acquired on March 18, 2013). The method uses six bands for transformation to calculate intensity of cloud and cloud shadows from the nine spectral bands and was further removed. The method takes advantage of spectral information. The validation demonstrates that cloud and cloud shadows contaminated pixels were accurately detected with overall accuracies of 98 and 97%, respectively. However, for thick cloud and cloud shadows, the performance of this method was limited. With further development there is potential for this method using for atmospheric corrections to improve landscape change detection.

As widely used today, high resolution image becomes a useful data source for forest inventory because it can show detailed information of land-cover types which is so helpful in interpreting process. And in the application of high-resolution images, the toughest problem is to find the effective characteristics group to separate each class accurately. In this paper, we tried an object-based method to get the whole forest distribution of the study area. Combining segmentation and decision tree feature selection tool, we tried to find a convenient and effective way to select useful information from such many characteristics brought by ―super-pixels‖ after segmentation. Compared with the traditional pixel-based classification method, we found that object-based method was more appropriate not only for its nearly 10% higher classification accuracy but also providing with more detailed information lying in the image data that help.

The paper gives a quantitative analysis on soil erosion in Shanchonghe watershed. The quantitative analysis is based on a revised universal soil loss equation (RUSLE), supported by geographic information system (GIS) and remote sensing (RS) technology, and according to the landform, rainfall, vegetation data, etc., in Shanchonghe watershed, which are obtained from interpreted data of Shanchonghe watershed RS image and it’s statistics data. The results showed that the annual soil erosion modulus in Shanchonghe watershed is 19.05 t / (hm2*a), the annual soil erosion amount is 2744.23 t/a. The soil erosion spatial distribution is very different. These areas have strong soil erosion, which height is more than 2000 meters above sea level, or which slope is bigger than 25 degree, or bare surfaces, or sloping farmlands. These are the focus areas of governance of soil erosion in Shanchonghe watershed. The study provides scientific basis to water and soil conservation work in Shanchonghe watershed.

A novel image fusion algorithm based on region segmentation and multiresolution analysis(MRA) is
proposed to make full use of advantages of different multiscale transform. Nonsubsampled
contourlet transform(NSCT) processes edges better than wavelet transform does. While wavelet
transform handles smooth area and singularities better than NSCT does. As an image often includes
more than one feature, the proposed method is conducted on the basis of Gaussian mixture
model(GMM) based region segmentation. Firstly, transform the multispectral(MS) image into
intensity, hue and saturation component. Secondly, segment intensity component into dense contour
and smooth regions according to GMM and NSCT. And then gain new intensity component by
fusing intensity component and high resolution image with Àtrous wavelet transform(ATWT) fusion
in smooth areas and NSCT fusion in dense contour areas. Finally transform the new intensity
together with hue component, saturation component back into RGB space and obtain the fused
image. Multisource remote sensing images are tested to assess this proposed algorithm. Visual
evaluation and statistics analysis are employed to evaluate the quality of fused images of different
methods. The proposed improved algorithm demonstrates excellent spectrum information and high
resolution. Experiment results show that the new proposed fusion algorithm incorporating with
region segmentation based improved GMM and MRA outperforms those algorithms based on
single multiscale transform.

Medium Resolution Spectral Imager (MERSI) on board China's new generation polar orbit meteorological satellite FY- 3A provides a new data source for snow monitoring in large area. As a case study, the typical snow cover of Qilian Mountains in northwest China was selected in this paper to develop the algorithm to map snow cover using FY- 3A/MERSI. By analyzing the spectral response characteristics of snow and other surface elements, as well as each channel image quality on FY-3A/MERSI, the widely used Normalized Difference Snow Index (NDSI) was defined to be computed from channel 2 and channel 7 for this satellite data. Basing on NDSI, a tree-structure prototype version of snow identification model was proposed, including five newly-built multi-spectral indexes to remove those pixels such as forest, cloud shadow, water, lake ice, sand (salty land), or cloud that are usually confused with snow step by step, especially, a snow/cloud discrimination index was proposed to eliminate cloud, apart from use of cloud mask product in advance. Furthermore, land cover land use (LULC) image has been adopted as auxiliary dataset to adjust the corresponding LULC NDSI threshold constraints for snow final determination and optimization. This model is composed as the core of FY-3A/MERSI snow cover mapping flowchart, to produce daily snow map at 250m spatial resolution, and statistics can be generated on the extent and persistence of snow cover in each pixel for time series maps. Preliminary validation activities of our snow identification model have been undertaken. Comparisons of the 104 FY- 3A/MERSI snow cover maps in 2010-2011 snow season with snow depth records from 16 meteorological stations in Qilian Mountains region, the sunny snow cover had an absolute accuracy of 92.8%. Results of the comparison with the snow cover identified from 6 Terra/MODIS scenes showed that they had consistent pixels about 85%. When the two satellite resultant snow cover maps compared with the 6 supervise-classified and expert-verified snow cover maps derived from integrated MERSI and MODIS images, we found FY-3A/MERSI has higher accuracy and stability not only for nearly cloud-free scenes but also the cloud scenes, namely, FY-3A/MERSI data can objectively reflect finer spatial distribution of snow and its dynamic development process, and the snow identification model perform better in snow/cloud discrimination. However, the ability of the FY-3A/MERSI model to discriminate thin snow and thin cloud need to be refined. And the limitation, error sources of FY-3A/MERSI snow products would be assessed based on the accumulation of large amounts of data in the future.

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Super-pixel as an object-oriented segmentation method has been widely used in image analysis. SLIC super-pixel utilizes the self-similarity of pixel color feature in natural scene as well as the edge information constraint between different regions, segments image through iteratively clustering, and obtains the corresponding homogeneous region. But for typical terrain classification, the color and intensity of a pixel do not have the consistency, this may lead to a problem of misclassification. Compared with the color and intensity features of pixels, the local texture features have a better description for the class attribute of pixels. In this paper, we use the random projection to extract texture feature from local image patches, combined with the advantage of compact structure and excellent homogeneity of SLIC segmentation, we propose a random projection super-pixel segmentation method, then utilize K-means to cluster the segmented patches in order to implement image classification. Experiments show that compared with the SLIC superpixel segmentation, the patches segmented by the random projection super-pixel segmentation is better to reflect the texture information of images, and the classification accuracy has been greatly improved.

As for SAR image, it has a relatively great geometric distortion, and contains a lot of speckle noise. So a lot of research has been done to find a good method for SAR image matching. SIFT (Scale Invariant Feature Transform) has been proved to a good algorithm for the SAR image matching. This operator can dispose of matching problem such as rotation, affine distortion and noise. In this passage, firstly, in the preprocessing process, we use BM3D to denoise the image which can perform well comparing to other denoise method. Then, regardless of traditional SIFT-RANSAC method, SIFT-TC is used to complete image matching. By using this method, the image matching is proved to have better predominance in the matching efficiency, speed and robustness.

A novel adaptive aircraft detection method based on level set processing and circle-frequency filter is proposed in this paper. First, the SBGFRLS (Selective Binary and Gaussian Filtering Regularized Level Set) method is used twice to find airport region of interest (ROI) and candidate aircraft areas by local segmentation and global segmentation, respectively, so that sizes of those possible target areas can be computed. Then, the circle-frequency (CF) filter method is utilized adaptively to detect target aircrafts in the airport ROI via the mean radius estimated by sizes of those candidate areas obtained before. Experimental results on real remote sensing airport images demonstrate the efficiency and accuracy of the proposed method.

Automatic remotely sensed glacier mapping in high mountainous areas is restricted due to confusion of glacier and snow. Most of current methods map glacier boundaries with a single remote sensing image, but it is hard to find one snow-free one cloud-free image. The paper presents an object-oriented image segmentation to delineate the full glacier extents with multi-temporal Landsat images and digital elevation models (DEM). Landsat images with different acquisition dates are limited within one or two year, so as to map the glacier extents with minimum snow coverage. Topographic features derived from DEMs and different solar angles are also used to separate mountain shadows from glaciers, so the glaciers shaded by mountain shadows can also be identified. The method is tested with 6 Landsat images (2009-2010) and SRTM DEM data in Bogeda Mountain of Tienshan Mountain, Xinjiang ,China. It showed that the minimum glacier extents derived with the proposed method can accurately match the SPOT-5 glacier map, and the geometric accuracy is less than 30 meters. Results are satisfying for annual glacier mapping for glacier change detection studies.

To ensure the attack mission success rate, a trajectory with high survivability and accepted path length and multiple paths with different attack angles must be planned. This paper proposes a novel path planning algorithm based on orthogonal particle swarm optimization, which divides population individual and speed vector into independent orthogonal parts, velocity and individual part update independently, this improvement advances optimization effect of traditional particle swarm optimization in the field of path planning, multiple paths are produced by setting different attacking angles, this method is simulated on electronic chart, the simulation result shows the effect of this method.

Snow disaster in pastoral area is a meteorological disaster which due to snowfall and snow covers, in relation to cowman’s product and life, seriously affects development of animal husbandry in China. So it has been paid high degree attention in the field of disaster prevention and reduction. Risk warning of snow disaster is an effective way to reduce the loss. Space technology, as an important way to monitor snow cover in pastoral area, could be used in risk warming of snow disaster. In this article, based on interpretation of remote sensing image, combined with geo-spatial, meteorological and land use data, research on three indicators have been carried out, which are: possibly degree of buried graze by snow, continuous days of perpetual snow and rate of perpetual snow, model on risk warning of snow disaster has been set up successfully. Case analysis in Inner Mongolia during 2012 shows that: the proposal model has a high precision and could be used in risk warning of snow disaster in pastoral area.

In this paper, we explored the application of computer vision technology to automatically detect the tasseling stage of maize. The commonly used HOG/SVM detection framework is chosen to recognize the ears of maize for determining the occurrence time of the stage. However, it cannot guarantee high precision rate. Thus, we proposed a new method called Spatio-temporal Saliency Mapping to highlight the ear while suppress the background, which significantly improve the detection performance. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. Monitoring cotton growth status by automatic image-based detection makes sense due to its low-cost, low-labor and the capability of continuous observations. However, little research has been done to improve close observation of different growth stages of field crops using digital cameras. Therefore, algorithms proposed by us were developed to detect the growth information and predict the starting date of cotton automatically. In this paper, we introduce an approach for automatic detecting five true-leaves stage, which is a critical growth stage of cotton. On account of the drawbacks caused by illumination and the complex background, we cannot use the global coverage as the unique standard of judgment. Consequently, we propose a new method to determine the five true-leaves stage through detecting the node number between the main stem and the side stems, based on the agricultural meteorological observation specification. The error of the results between the predicted starting date with the proposed algorithm and artificial observations is restricted to no more than one day.

Drought is one kind of nature disasters in the world. It has characteristics of temporal-spatial inhomogeneity, wide affected areas and periodic happening. The economic loss and affected population caused by different droughts are the largest in all natural disasters. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster risk assessment and monitoring. In this paper, three drought monitoring models, such as Vegetation Condition Index (VCI), Temperature Vegetation Dryness Index (TVDI), and Water Supplying Vegetation Index (WSVI) had been selected to monitor the drought occurred from January 2012 to June 2012 in Hubei province, China. Two kinds of remote sensing data, including HJ-1A/B CCD/IRS and ZY-3, had been employed to assess the integrated risk of Hubei drought based on three drought monitoring models. The results shown that the risk of northwest regions and middle regions in Hubei province were higher than that in the other regions. The results also indicated that the extreme risk regions were located in Shiyan, Xiangyang, Suizhou and Jingmen.

We present a new method for full-automatic calibration of traffic cameras using the end points on dashed lines. Our approach uses the improved RANSAC method with the help of pixels transverse projection to detect the dashed lines and end points on them. Then combining analysis of the geometric relationship between the camera and road coordinate systems, we construct a road model to fit the end points. Finally using two-dimension calibration method we can convert pixels in image to meters along the ground truth lane. On a large number of experiments exhibiting a variety of conditions, our approach performs well, achieving less than 5% error in measuring test lengths in all cases.

Poyang Lake Basin is the biggest freshwater lake in China and a significant wetland of the world. The study of the land use changes on there is a great significance for regional sustainable development. In this paper, using the RS image as the main data source, the study area was divided into six land use types. With the acquired land use data of three periods, the spatio-temporal dynamic variation characteristics were analyzed with the area changes and land use dynamic index (LUDI). The analysis shows that the largest land use type is woodland, followed by cultivated land and grassland, and area of the rest types all account for less than 10%. Through the analysis of the area transfer matrix of LUCC, it shows that woodland and construction land increased in each period while cultivated land reduced. Unused land increased a lot during 1990 to 2000 before decreased dramatically during 2000 to 2008, grassland and water experienced a significant increase after an obvious decline and came out to increase finally. The analysis of LUDI indicates that construction land changed the most quickly, followed by unused land and cultivated land, yet the other land use types changed slowly.

We demonstrate some metamaterials in the terahertz frequency regime fabricated on n-GaAs substrate. The artificially structured electromagnetic materials, which called metamaterials, has led to the realization of phenomena which cannot be obtained with natural materials. We have found many fundamental progress and applications of metamaterials in millimeter wave or microwave, and many usefully potential applications of terahertz as well, but still need considerable efforts to fill this “THz-gap” in future. Therefore, it is especially important to design the metamaterials device in the terahertz frequency regime. For the first, we analyze the split ring resonators(SRRs) model in theory, and many planar SRR arrays with different periodic structures have been designed for later testing, the planar SRR arrays are fabricated using conventional photolithography and electron-beam deposition of gold on n-GaAs substrate, the metal array and n- GaAs together form a Schottky contact. Then the influences of background substrate and the shapes of the SRRs on the terahertz resonance are experimentally investigated at several terahertz frequencies of continuous wave terahertz laser in turn, and all the transmission properties are recoded and analyzed. These metamaterials may be useful for future applications in the construction of various THz filters, THz antenna, or THz grid structures ideal for constructing THz switching devices.

A novel backward elimination algorithm (BEA) based on the energy contributions of the non-orthogonal (coupled) regressor vectors is introduced for radial basis function (RBF) neural network construction. This algorithm builds RBF model by eliminating the minimum contribution regressor term among all candidates according to mean predictedresidual- sums-of-squares (PRESS) error. It first generates an initial model using computationally affordable batch learning and then updated it by a sequent learning with new training samples arriving. During the whole learning, the network architecture always remains the most optimal. This also can assure a better RBF network even if the RBF original basis is non-orthogonal. The effectiveness of new algorithm is demonstrated by the simulated results.

A modularized reconfigurable system for target recognition with multi-DSP processing is designed to reconfigure the target recognition modules and update the distributed target feature libraries through the serial channel to adapt to the varied application. The system is separated into three independent modules and two work modes running at different time slides based on project switch. The modularized reconfiguration module is designed as a minimum security kernel separated from the target recognition module to decrease their coupling and interrelationship. This kind of multi-project design based on cyclic redundancy check presents a more independent and reliable target recognition system with modularized reconfiguration ability.

License plate localization (LPL) in open environment is quite challenging due to plate variations and environment variations. In this paper a new algorithm for license plate localization based on color pair and stroke width features of character is proposed. Four steps are mainly concerned in our algorithm. The image is first preprocessed by canny edge detector and color pair feature is extracted. And then edge pixels are clustered into several groups using by EM-based method. Further more, stroke width feature of edge pixels in each group are extracted to remove false groups and background outliers. Finally, LP candidates can be formed by morphological operation and prior knowledge of LP is used for verification and accurate location. We use a standard dataset including natural scene images with background noise, various observation views, changing illumination and various plate sizes for testing. The results show that the proposed algorithm achieves accuracy over 90% on localizing license plate and the processing time is 250ms in average for one image with size of 640*480.

A communication system of the smart grid based on the partial reconfiguration technology was provided dependent on the analysis of the principle and the requirements of the smart grid. The system can dynamically update the smart grid protocol and interface design during normal operating in order to avoid unnecessary outage. While the use of the partial reconfiguration technology reduces the occupation rate of the FPGA resource, increase the flexibility of the system, and decrease the research the design time for the system. Moreover, the general architecture based on the partial reconfiguration technology can be used in various remote control device of the smart grid.

It is a simple and efficient way to complete the custom program according to the GIS spatial analysis function supported by ArcGIS Geoprocessing. In this paper, the ArcGIS Geoprocessing technology based on the plug-in GIS Secondary development is used to build the drought remote sensing monitoring system. The 2006 drought data in Chongqing was processed as an example for this system, and drought monitoring models suitable for Chongqing were selected. Compared to the traditional manual processing, the system’s automation degree has been greatly improved and enhanced, which can accurate decision-making basis for the relevant departments.

The 8-bit PIC core MCU SOC chip integrates three main functions in a single chip providing optimal solutions for various fiber module applications. The functional blocks main include a 2.5Gbps Post-Amplifier (PA), a 1.25Gbps burst or continuous mode Laser-Driver (LD), and an 8-bit PIC-core MCU. The PA receives differential signal and performs the quantization amplifications. The LD driver performs the transmit function. The on-chip MCU is the flexibility and versatility of controlling and configurations of fiber module design. The main purpose of the article is that the system digital peripherals design, such as I2C slave for host communication and its associated SFP 2-port RAM, two levels of password protection for SFP 512-byte registers, and I2C master for EEPROM interface, and the access to on-chip 2K program SRAM, and boot ROM code process.

Gravity gradient is a tensor with five mutual independent components. Five gravity gradient components are complementary. Combining the gravity gradient full tensor, more detail information is contributed to gravity gradient matching aided position. Gravity gradient full tensor fusion matching aided position method is proposed in this paper. The matching strategy is particle filtering (PF) and fusion strategy is weighted fusion on the confidence coefficient of each gravity gradient component. Simulations have been done and results showed that full tensor fusion matching aided position method is more effective than the aided position method based on single gravity gradient component.

In this paper, use the data of FY3A equipped with medium-resolution spectral imager (MERSI), base on drought monitoring model (PDI、VSWI and MSAVI) to drought monitoring and analysis in the eastern part of Gansu Province,2012, combined with meteorological part of precipitation, temperature, and meteorological drought data were compared the analysis showed that the drought monitoring model based on the inversion of FY3A/MERSI satellite data can objectively reflect the spatial distribution of drought in the study area and the dynamic development process, significantly different model of monitoring the appropriate drought monitoring and differences; The drought level was verified by the soil moisture obtained in field work. The relationship of drought severity and soil moisture with soli depth of 0-10cm, 0-20cm and 0-40cm was shown: There is a good relationship between the three drought indexes and the corresponding soil moisture.

In this paper, we design a hand-shape image acquisition and processing system based on DSP for solving the problem of hand-shape recognition. Acquisition configuration was designed, and the key parts (encoder, decoder, memory chip etc.) are selected. And a new method for hand-shape recognition based on wavelet moment is presented to overcome some shortage in present method for hand shape recognition. Firstly, image processing including binary processing and segment of hand silhouette are used, and then translation and scale normalization algorithms is implemented on the palms and fingers image. After that the wavelet moment characteristics of image are extracted. At last, support vector is achieved by training 100 images data in images database, 10 testing images were selected randomly to verify validity and feasibility of algorithms. Experimental results indicate that the 10 testing images are all classified correctly. The new method of extracting hand shape wavelet moment as characteristic matrix is easy to realize with characteristic of high utility and accuracy, and solve the problem of translation, rotation and scaling during the image acquisition process without positioning aids.

By using optical image processing techniques, a novel text encryption and hiding method applied by double-random phase-encoding technique is proposed in the paper. The first step is that the secret message is transformed into a 2- dimension array. The higher bits of the elements in the array are used to fill with the bit stream of the secret text, while the lower bits are stored specific values. Then, the transformed array is encoded by double random phase encoding technique. Last, the encoded array is embedded on a public host image to obtain the image embedded with hidden text. The performance of the proposed technique is tested via analytical modeling and test data stream. Experimental results show that the secret text can be recovered either accurately or almost accurately, while maintaining the quality of the host image embedded with hidden data by properly selecting the method of transforming the secret text into an array and the superimposition coefficient.

The paper studies effective ways to sharpen images by using of partial differential equation and the classical image sharpening operators. Based on the physical meaning of heat equation, by regarding the gray values of an image as the temperature on a flat object, a model on anti-heat equation is obtained. Under the assumptions of the blurred image is the result of the original image filtered by the heat equation and the time t is small enough, we establish a model for image sharpening, which is solved by means of Sobel operator. The results of numerical experiments show that the method can make the image sharper and obtain clearer image. Sharpened image hasn’t moved information, at the same time noise doesn’t appear, it does keep the edges and details in the original image. The algorithm maintains stably over a long time, that is, the sharpened image does not been blurred again with the increase of the number of iterations.

Video surveillance is an important security technology, which is widely use in public areas and police detection utilizing video surveillance system is a powerful method in the police investigation that must answer the question of five W’s on crim. In this paper, a HWMSE system is designed to solve problems in case detection based on the theories of metasynthesis from qualitative to quantitative. The proposed HWMSE system is constituted of four components: Database sub-system of case information and knowledge, man-machine combination toolsets of video analysis, deduction simulation system based on crime behavior modeling and case analyzing platform.